Device management strategies are becoming more intelligent utilizing the increasing processing power available on board and richer data from more sophisticated instrumentation. One approach to managing this complexity is to apply artificial intelligence and adaptive algorithms which optimize the performance or report problems based on the performance trends of the individual device or a group of devices.
While it is relatively simple to incorporate local instrumentation data into intelligent algorithms decision making, it is difficult to aggregate the data of whole networks of devices to better inform local decision making. The key difficulties are that customer secure networks are not typically available to the terminal for open peer to peer communication or for vendor controlled software updates. Typically any communications over the customer operated networks must be part of formally released customer updates which limits the flexibility and frequency of update.
Therefore a problem with the existing systems is how to share some form of aggregated performance data between peers to allow them to determine what the correct reporting or action thresholds of behavior are for achieving optimal reliability.
For example, a customer engineer may service thirty to forty automated teller machines (ATMs) with their own specific geographic territory. These ATMs may be divided between two to more financial institutions. Whilst the ATMs of one financial institution may share diagnostic information via a central management system via the financial institution's secure network. However, obtaining sufficient granularity, for example on a geographic basis, to set thresholds for instigating actions, such as customer engineer call out or taking a device out of operation, due to problems occurring can prove difficult where each financial institution has a limited number of ATMs within a particular environment. For example, a financial institution may have thousands of ATMs across the U.S. but, for example, only five in Alaska, cold related problems in Alaska will not show as a major problem within the institution's network. However, across the ATMs of all financial institutions operating within, for example, Alaska, there will be a great richness of data in relation to cold related problems. However, due to the requirement for security in financial institutions and the consequent isolated nature of these institutions' networks the richness of this data cannot currently be captured.
Allied with the capture of granular performance data is the ability to set local performance thresholds based upon this local performance data. However, this is not currently practical due to there possibly only being a few ATMs in each local area for each financial institution.